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legacy/v1/PHASE2_INFLUXDB.md
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legacy/v1/PHASE2_INFLUXDB.md
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# Phase 2: InfluxDB Integration - Roadmap
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## Ziel
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Nutzung historischer Verbrauchsdaten aus InfluxDB2 für:
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- Bessere Verbrauchsprognosen
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- Optimierte Ladeplanung
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- Lernender Algorithmus
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## Datenquellen in InfluxDB
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### Zu analysierende Daten
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**Verbrauch**
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- `sensor.house_consumption` (Hausverbrauch in W)
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- `sensor.totay_load` (Tages-Gesamtverbrauch)
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- `sensor.bought_from_grid_today` (Netzbezug)
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**Erzeugung**
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- `sensor.pv_power` (PV-Leistung)
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- `sensor.today_s_pv_generation` (Tagesertrag)
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**Batterie**
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- `sensor.battery_power` (Ladung/Entladung)
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- `sensor.battery_state_of_charge` (SOC)
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- `sensor.today_battery_charge` (Geladen heute)
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- `sensor.today_battery_discharge` (Entladen heute)
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**Netz**
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- `sensor.gw_netzbezug` (Bezug)
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- `sensor.gw_netzeinspeisung` (Einspeisung)
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## Implementierungsschritte
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### Schritt 1: InfluxDB Verbindung in PyScript
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```python
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"""
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InfluxDB Connector für historische Daten
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Speicherort: /config/pyscript/influxdb_connector.py
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"""
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from influxdb_client import InfluxDBClient
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from datetime import datetime, timedelta
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# Konfiguration (später in configuration.yaml)
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INFLUXDB_URL = "http://your-influxdb-server:8086"
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INFLUXDB_TOKEN = "your-token"
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INFLUXDB_ORG = "your-org"
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INFLUXDB_BUCKET = "home_assistant"
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@service
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def get_historical_consumption(days: int = 30):
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"""
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Holt historische Verbrauchsdaten aus InfluxDB
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Args:
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days: Anzahl vergangener Tage
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Returns:
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Dict mit stündlichen Durchschnittswerten
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"""
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client = InfluxDBClient(
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url=INFLUXDB_URL,
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token=INFLUXDB_TOKEN,
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org=INFLUXDB_ORG
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)
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query_api = client.query_api()
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# Flux Query für stündliche Durchschnittswerte
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query = f'''
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from(bucket: "{INFLUXDB_BUCKET}")
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|> range(start: -{days}d)
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|> filter(fn: (r) => r["entity_id"] == "house_consumption")
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|> filter(fn: (r) => r["_field"] == "value")
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|> aggregateWindow(every: 1h, fn: mean, createEmpty: false)
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|> yield(name: "mean")
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'''
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result = query_api.query(query)
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# Verarbeite Ergebnisse nach Wochentag und Stunde
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consumption_by_hour = {}
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for table in result:
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for record in table.records:
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timestamp = record.get_time()
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value = record.get_value()
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weekday = timestamp.weekday() # 0=Montag, 6=Sonntag
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hour = timestamp.hour
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key = f"{weekday}_{hour}"
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if key not in consumption_by_hour:
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consumption_by_hour[key] = []
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consumption_by_hour[key].append(value)
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# Berechne Durchschnittswerte
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avg_consumption = {}
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for key, values in consumption_by_hour.items():
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avg_consumption[key] = sum(values) / len(values)
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client.close()
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log.info(f"Historische Daten geladen: {len(avg_consumption)} Stunden-Profile")
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return avg_consumption
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```
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### Schritt 2: Erweiterte Verbrauchsprognose
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```python
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def predict_consumption(start_time, hours=24):
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"""
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Prognostiziert Verbrauch basierend auf historischen Daten
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Args:
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start_time: Startzeit der Prognose
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hours: Anzahl Stunden
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Returns:
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Dict mit stündlichen Verbrauchsprognosen
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"""
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# Lade historische Daten (gecacht)
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if not hasattr(predict_consumption, 'historical_data'):
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predict_consumption.historical_data = get_historical_consumption(30)
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historical = predict_consumption.historical_data
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forecast = {}
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for h in range(hours):
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dt = start_time + timedelta(hours=h)
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weekday = dt.weekday()
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hour = dt.hour
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key = f"{weekday}_{hour}"
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# Durchschnittlicher Verbrauch für diese Wochentag/Stunde
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avg_consumption = historical.get(key, 800) # Fallback 800W
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# Saisonale Anpassungen
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month = dt.month
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if month in [12, 1, 2]: # Winter
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avg_consumption *= 1.2
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elif month in [6, 7, 8]: # Sommer
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avg_consumption *= 0.9
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forecast[dt] = avg_consumption
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return forecast
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```
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### Schritt 3: Optimierung mit Verbrauchsprognose
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```python
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def optimize_charging_schedule_v2(price_data, pv_forecast, battery_state, config):
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"""
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Erweiterte Optimierung mit Verbrauchsprognose
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"""
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schedule = {}
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# NEU: Verbrauchsprognose holen
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consumption_forecast = predict_consumption(datetime.now(), hours=48)
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# Sortiere Preise
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sorted_prices = sorted(price_data.items(), key=lambda x: x[1])
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threshold = calculate_price_threshold(price_data, config)
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# Batterie-Simulation
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current_energy_kwh = (battery_state['soc'] / 100.0) * config['battery_capacity']
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for dt, price in sorted(price_data.items()):
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if dt <= datetime.now():
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continue
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# PV und Verbrauch für diese Stunde
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pv_kwh = pv_forecast.get(dt, 0)
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consumption_w = consumption_forecast.get(dt, 800)
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consumption_kwh = consumption_w / 1000.0
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# Berechne Energie-Bilanz
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net_energy = pv_kwh - consumption_kwh
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# Entscheidung: Laden oder nicht?
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action = 'auto'
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power_w = 0
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reason = []
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if price <= threshold:
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# Prüfe ob Batterie-Kapazität benötigt wird
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max_capacity_kwh = (config['max_soc'] / 100.0) * config['battery_capacity']
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available_capacity = max_capacity_kwh - current_energy_kwh
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# Erwartetes Defizit in den nächsten 6 Stunden
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future_deficit = calculate_future_deficit(
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dt, consumption_forecast, pv_forecast, hours=6
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)
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# Lade wenn:
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# 1. Günstiger Preis
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# 2. Defizit erwartet
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# 3. Kapazität vorhanden
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if future_deficit > 0.5 and available_capacity > 0.5:
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action = 'charge'
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charge_kwh = min(available_capacity, future_deficit,
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config['max_charge_power'] / 1000.0)
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power_w = -int(charge_kwh * 1000)
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current_energy_kwh += charge_kwh
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reason.append(f"Defizit erwartet: {future_deficit:.1f} kWh")
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# Update Batterie-Stand für nächste Iteration
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current_energy_kwh += net_energy
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current_energy_kwh = max(
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(config['min_soc'] / 100.0) * config['battery_capacity'],
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min(current_energy_kwh, max_capacity_kwh)
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)
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schedule[dt.isoformat()] = {
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'action': action,
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'power_w': power_w,
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'price': price,
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'pv_forecast': pv_kwh,
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'consumption_forecast': consumption_kwh,
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'net_energy': net_energy,
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'battery_soc_forecast': (current_energy_kwh / config['battery_capacity']) * 100,
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'reason': ', '.join(reason)
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}
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return schedule
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def calculate_future_deficit(start_dt, consumption_forecast, pv_forecast, hours=6):
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"""
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Berechnet erwartetes Energie-Defizit in den nächsten X Stunden
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"""
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total_deficit = 0
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for h in range(hours):
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dt = start_dt + timedelta(hours=h)
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consumption_w = consumption_forecast.get(dt, 800)
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pv_kwh = pv_forecast.get(dt, 0)
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consumption_kwh = consumption_w / 1000.0
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net = consumption_kwh - pv_kwh
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if net > 0:
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total_deficit += net
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return total_deficit
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```
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### Schritt 4: Konfiguration erweitern
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```yaml
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# Neue Input Helper für InfluxDB
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input_text:
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influxdb_url:
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name: "InfluxDB URL"
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initial: "http://192.168.xxx.xxx:8086"
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influxdb_token:
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name: "InfluxDB Token"
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initial: "your-token"
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influxdb_org:
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name: "InfluxDB Organization"
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initial: "homeassistant"
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influxdb_bucket:
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name: "InfluxDB Bucket"
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initial: "home_assistant"
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input_number:
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historical_data_days:
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name: "Historische Daten (Tage)"
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min: 7
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max: 365
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step: 1
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initial: 30
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icon: mdi:calendar-range
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```
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### Schritt 5: Neue Automatisierung
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```yaml
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automation:
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# Wöchentliches Update der historischen Daten
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- id: battery_optimizer_update_historical_data
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alias: "Batterie Optimierung: Historische Daten aktualisieren"
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description: "Lädt wöchentlich neue historische Daten aus InfluxDB"
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trigger:
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- platform: time
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at: "03:00:00"
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- platform: time_pattern
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# Jeden Sonntag
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days: /7
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action:
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- service: pyscript.get_historical_consumption
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data:
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days: 30
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- service: notify.persistent_notification
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data:
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title: "Batterie-Optimierung"
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message: "Historische Daten aktualisiert"
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```
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## Metriken & KPIs
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### Neue Dashboard-Elemente
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```yaml
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template:
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- sensor:
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- name: "Verbrauchsprognose Genauigkeit"
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unique_id: consumption_forecast_accuracy
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state: >
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{% set actual = states('sensor.today_load') | float %}
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{% set forecast = state_attr('input_text.battery_charging_schedule', 'total_consumption_forecast') | float %}
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{% if forecast > 0 %}
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{{ ((1 - abs(actual - forecast) / forecast) * 100) | round(1) }}
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{% else %}
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unknown
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{% endif %}
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unit_of_measurement: "%"
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icon: mdi:target
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- name: "Optimierungs-Einsparung"
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unique_id: optimizer_savings
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state: >
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# Berechne tatsächliche Kosten vs. ohne Optimierung
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# TODO: Implementierung basierend auf InfluxDB Daten
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unit_of_measurement: "EUR"
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icon: mdi:piggy-bank
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```
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## Erwartete Verbesserungen
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### Genauigkeit
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- **Verbrauchsprognose**: +40% durch historische Daten
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- **Ladeplanung**: +25% durch bessere Vorhersage
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- **ROI**: Messbare Einsparungen
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### Intelligenz
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- Wochenend-Muster erkennen
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- Saisonale Anpassungen
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- Feiertags-Berücksichtigung
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### Adaptivität
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- System lernt aus Fehlprognosen
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- Automatische Parameter-Anpassung
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- Kontinuierliche Verbesserung
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## Nächste Schritte
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1. **InfluxDB Setup prüfen**
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- Sind alle Sensoren geloggt?
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- Retention Policy konfiguriert?
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- Genug historische Daten (min. 30 Tage)?
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2. **Connector implementieren**
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- InfluxDB Client installieren: `pip install influxdb-client`
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- Token und Zugangsdaten konfigurieren
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- Erste Testabfrage durchführen
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3. **Verbrauchsmuster analysieren**
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- Wochentag vs. Wochenende
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- Tagesverlauf typisch?
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- Saisonale Unterschiede?
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4. **Integration testen**
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- Mit historischen Daten simulieren
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- Prognose-Genauigkeit messen
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- Schrittweise in Produktion nehmen
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5. **Dashboard erweitern**
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- Prognose vs. Ist-Verbrauch
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- Einsparungen visualisieren
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- Lernkurve anzeigen
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## Fragen für Phase 2
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Bevor wir starten:
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1. **InfluxDB**: Ist bereits konfiguriert? Zugangsdaten?
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2. **Daten-Historie**: Wieviel Tage sind verfügbar?
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3. **Sensoren**: Welche sind in InfluxDB geloggt?
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4. **Retention**: Wie lange werden Daten behalten?
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5. **Performance**: Wie groß ist die Datenbank?
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Lass uns das in einem nächsten Schritt gemeinsam analysieren!
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---
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**Status**: Phase 1 abgeschlossen ✅
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**Nächster Meilenstein**: InfluxDB Integration 🎯
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||||
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